SoroushVahidi/scidocs huggingface dataset:
Black cattle body shape and temperature measurement using thermography and KINECT sensor
(2017)

Black cattle body shape and temperature measurement system is introduced. It is important to evaluate the quality of Japanese black cattle periodically during their growth process. Not only the weight and size of cattle, but also the posture, shape, and temperature need to be tracked as primary evaluation criteria. In the present study, KINECT sensor and thermal camera obtains the body shape and its temperature. The whole system is calibrated to operate in a common coordinate system. Point cloud data are obtained from different angles and reconstructed in a computer. The thermal data are captured too. Both point cloud data and thermal information are combined by considering the orientation of the cow. The collected information is used to evaluate and estimate cattle conditions.
Adaptive Mobile Robot Navigation and Mapping
(1999)

The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman lter to incorporate vehicle localization and environmental mapping. We describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. We introduce a metric for adaptive sensing which is de ned in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action which yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for 1) adaptive control of motion and 2) adaptive control of motion and scanning. The vehicle tends to explore selectively di erent objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.
A comparison of track-to-track fusion algorithms for automotive sensor fusion
(2008)

In exteroceptive automotive sensor fusion, sensor data are usually only available as processed, tracked object data and not as raw sensor data. Applying a Kalman filter to such data leads to additional delays and generally underestimates the fused objectspsila covariance due to temporal correlations of individual sensor data as well as inter-sensor correlations. We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion of sensor objects of unknown correlation, namely covariance union, covariance intersection, and use of cross-covariance. For the simulation setup used in this paper, covariance intersection and use of cross-covariance turn out to yield significantly lower errors than a Kalman filter at a comparable computational load.
The Study of a Dual-Mode Ring Oscillator
(2011)

An analytical investigation of a dual-mode ring oscillator is presented. The ring oscillator is designed using a CMOS 0.18-μm technology with differential eight-stage delay cells employing auxiliary input devices. With a proper startup control, the oscillator operates in two different modes covering two different frequency bands. A nonlinear model, along with the linearization method, is used to obtain the transient and steady-state behaviors of the dual-mode ring oscillator. The analytical derivations are verified through HSPICE simulation. The oscillator operates at the frequency bands from 2-5 GHz and from 0.1-2 GHz, respectively.
Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil
(2015)

Deep Learning is one of the latest approaches in the field of artificial neural networks. Since they were first proposed in mid-2006, Deep Learning models have obtained state-of-art results in some problems with classification and pattern recognition. However, such models have been little used in time series forecasting. This work aims to investigate the use of some of these architectures in this kind of problem, specifically in predicting the hourly average speed of winds in the Northeastern region of Brazil. The results showed that Deep Learning offers a good alternative for performing this task, overcoming some results of previous works.
Characterizing large-scale use of a direct manipulation application in the wild
(2010)

Examining large-scale, long-term application use is critical to understanding how an application meets the needs of its user community. However, there have been few published analyses of long-term use of desktop applications, and none that have examined applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an instrumented version of the GNU Image Manipulation Program, including data from over 200 users. In the course of our analysis, we show that previous findings concerning the sparseness of command use and idiosyncrasy of users’ command vocabularies extend to a new domain and interaction style. These findings motivate continued research in adaptive and mixed-initiative interfaces. We also describe the novel application of a clustering technique to characterize a user community’s higher-level tasks from low-level logging data.
On traveling path and related problems for a mobile station in a rechargeable sensor network
(2013)

Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.
An LLCL Power Filter for Single-Phase Grid-Tied Inverter
(2012)

This paper presents a new topology of higher order power filter for grid-tied voltage-source inverters, named the LLCL filter, which inserts a small inductor in the branch loop of the capacitor in the traditional LCL filter to compose a series resonant circuit at the switching frequency. Particularly, it can attenuate the switching-frequency current ripple components much better than an LCL filter, leading to a decrease in the total inductance and volume. Furthermore, by decreasing the inductance of a grid-side inductor, it raises the characteristic resonance frequency, which is beneficial to the inverter system control. The parameter design criteria of the proposed LLCL filter is also introduced. The comparative analysis and discussions regarding the traditional LCL filter and the proposed LLCL filter have been presented and evaluated through experiment on a 1.8-kW-single-phase grid-tied inverter prototype.
Development of a SiC JFET-Based Six-Pack Power Module for a Fully Integrated Inverter
(2013)

In this paper, a fully integrated silicon carbide (SiC)-based six-pack power module is designed and developed. With 1200-V, 100-A module rating, each switching element is composed of four paralleled SiC junction gate field-effect transistors (JFETs) with two antiparallel SiC Schottky barrier diodes. The stability of the module assembly processes is confirmed with 1000 cycles of -40°C to +200°C thermal shock tests with 1.3°C/s temperature change. The static characteristics of the module are evaluated and the results show 55 mΩ on-state resistance of the phase leg at 200°C junction temperature. For switching performances, the experiments demonstrate that while utilizing a 650-V voltage and 60-A current, the module switching loss decreases as the junction temperature increases up to 150°C. The test setup over a large temperature range is also described. Meanwhile, the shoot-through influenced by the SiC JFET internal capacitance as well as package parasitic inductances are discussed. Additionally, a liquid cooled three-phase inverter with 22.9 cm × 22.4 cm × 7.1 cm volume and 3.53-kg weight, based on this power module, is designed and developed for electric vehicle and hybrid electric vehicle applications. A conversion efficiency of 98.5% is achieved at 10 kHz switching frequency at 5 kW output power. The inverter is evaluated with coolant temperature up to 95°C successfully.
Stability of photovoltaic and wind turbine grid-connected inverters for a large set of grid impedance values
(2006)

The aim of this paper is to analyze the stability problems of grid connected inverters used in distributed generation. Complex controllers (e.g., multiple rotating dq-frames or resonant-based) are often required to compensate low frequency grid voltage background distortion and an LCL-filter is usually adopted for the high frequency one. The possible wide range of grid impedance values (distributed generation is suited for remote areas with radial distribution plants) challenge the stability and the effectiveness of the LCL-filter-based current controlled system. It has been found out and it will be demonstrated in this paper that the use of active damping helps to stabilise the system in respect to many different kinds of resonances. The use of active damping results in an easy plug-in feature of the generation system in a vast range of grid conditions and in a more flexible operation of the overall system able to manage sudden grid changes. In the paper, a vast measurement campaign made on a single-phase system and on a three-phase system used as scale prototypes for photovoltaic and wind turbines, respectively, validate the analysis.
10 kV/120 A SiC DMOSFET half H-bridge power modules for 1 MVA solid state power substation
(2011)

In this paper, the extension of SiC power technology to higher voltage 10 kV/10 A SiC DMOSFETs and SiC JBS diodes is discussed. A new 10 kV/120 A SiC power module using these 10 kV SiC devices is also described which enables a compact 13.8 kV to 465/√3 solid state power substation (SSPS) rated at 1 MVA.
Overview of Control and Grid Synchronization for Distributed Power Generation Systems
(2006)

Renewable energy sources like wind, sun, and hydro are seen as a reliable alternative to the traditional energy sources such as oil, natural gas, or coal. Distributed power generation systems (DPGSs) based on renewable energy sources experience a large development worldwide, with Germany, Denmark, Japan, and USA as leaders in the development in this field. Due to the increasing number of DPGSs connected to the utility network, new and stricter standards in respect to power quality, safe running, and islanding protection are issued. As a consequence, the control of distributed generation systems should be improved to meet the requirements for grid interconnection. This paper gives an overview of the structures for the DPGS based on fuel cell, photovoltaic, and wind turbines. In addition, control structures of the grid-side converter are presented, and the possibility of compensation for low-order harmonics is also discussed. Moreover, control strategies when running on grid faults are treated. This paper ends up with an overview of synchronization methods and a discussion about their importance in the control
Facial recognition using histogram of gradients and support vector machines
(2017)

Face recognition is widely used in computer vision and in many other biometric applications where security is a major concern. The most common problem in recognizing a face arises due to pose variations, different illumination conditions and so on. The main focus of this paper is to recognize whether a given face input corresponds to a registered person in the database. Face recognition is done using Histogram of Oriented Gradients (HOG) technique in AT & T database with an inclusion of a real time subject to evaluate the performance of the algorithm. The feature vectors generated by HOG descriptor are used to train Support Vector Machines (SVM) and results are verified against a given test input. The proposed method checks whether a test image in different pose and lighting conditions is matched correctly with trained images of the facial database. The results of the proposed approach show minimal false positives and improved detection accuracy.
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
(2018)

In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shiftreduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models
Performance Analysis of Software-Defined Networking (SDN)
(2013)

Software-Defined Networking (SDN) approaches were introduced as early as the mid-1990s, but just recently became a well-established industry standard. Many network architectures and systems adopted SDN, and vendors are choosing SDN as an alternative to the fixed, predefined, and inflexible protocol stack. SDN offers flexible, dynamic, and programmable functionality of network systems, as well as many other advantages such as centralized control, reduced complexity, better user experience, and a dramatic decrease in network systems and equipment costs. However, SDN characterization and capabilities, as well as workload of the network traffic that the SDN-based systems handle, determine the level of these advantages. Moreover, the enabled flexibility of SDN-based systems comes with a performance penalty. The design and capabilities of the underlying SDN infrastructure influence the performance of common network tasks, compared to a dedicated solution. In this paper we analyze two issues: a) the impact of SDN on raw performance (in terms of throughput and latency) under various workloads, and b) whether there is an inherent performance penalty for a complex, more functional, SDN infrastructure. Our results indicate that SDN does have a performance penalty, however, it is not necessarily related to the complexity level of the underlying SDN infrastructure.
Applications of Lucid Dreaming in Sports
(2010)

The following article has above all a practical orientation. The various possibilities for the application of lucid dreaming in sports training are presented and briefly illustrated. These theses are based on findings from experiments with experienced lucid dreamers who were instructed to carry out various routine and sport-related actions while lucid-dreaming, with the object of observing the effects on both dreaming and waking states (Tholey, 1981a). Additionally, I report here on numerous spontaneous accounts of athletes who have mastered lucid dreaming, as well as my own years of experience as a lucid dreamer and as an active competitor in different types of sports.
A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes
(2017)

Received Nov 5, 2016 Revised Jan 9, 2017 Accepted Feb 18, 2017 Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization. Keyword:
Nursing bedside clinical handover - an integrated review of issues and tools.
(2015)

AIMS AND OBJECTIVES
This article reviews the available literature that supports implementing bedside clinical handover in nursing clinical practice and then seeks to identify key issues if any.


BACKGROUND
Clinical handover practices are recognised as being an essential component in the effective transfer of clinical care between health practitioners. It is recognised that the point where a patient is 'handed over' from one clinician to another is significant in maintaining continuity of care and that doing this poorly can have significant safety issues for the patient.


DESIGN
An integrated literature review.


METHOD
A literature review of 45 articles was undertaken to understand bedside clinical handover and the issues related to the implementation of this process.


RESULTS
It was identified that there are a number of clinical handover mnemonics available that provide structure to the process and that areas such as confidentiality, inclusion of the patient/carer and involving the multidisciplinary team remain topical issues for practitioners in implementing good clinical handover practices.


CONCLUSIONS
This literature review identified a lack of literature available about the transfer of responsibility and accountability during clinical handover and auditing practices of the clinical handover process. The nurses were more concerned about confidentiality issues than were patients. The use of a structured tool was strongly supported; however, no one singular tool was considered suitable for all clinical areas.


RELEVANCE TO CLINICAL PRACTICE
Nursing clinicians seeking to implement best practice within their professional speciality should consider some of the issues raised within this article and seek to address these issues by developing strategies to overcome them.
Forward Error Correction for DNA Data Storage
(2016)

We report on a strong capacity boost in storing digital data in synthetic DNA. In principle, synthetic DNA is an ideal media to archive digital data for very long times because the achievable data density and longevity outperforms today’s digital data storage media by far. On the other hand, neither the synthesis, nor the amplification and the sequencing of DNA strands can be performed error-free today and in the foreseeable future. In order to make synthetic DNA available as digital data storage media, specifically tailored forward error correction schemes have to be applied. For the purpose of realizing a DNA data storage, we have developed an efficient and robust forwarderror-correcting scheme adapted to the DNA channel. We based the design of the needed DNA channel model on data from a proof-of-concept conducted 2012 by a team from the Harvard Medical School [1]. Our forward error correction scheme is able to cope with all error types of today’s DNA synthesis, amplification and sequencing processes, e.g. insertion, deletion, and swap errors. In a successful experiment, we were able to store and retrieve error-free 22 MByte of digital data in synthetic DNA recently. The found residual error probability is already in the same order as it is in hard disk drives and can be easily improved further. This proves the feasibility to use synthetic DNA as longterm digital data storage media.
CLIP: Continuous Location Integrity and Provenance for Mobile Phones
(2015)

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.
Security and privacy in mobile crowdsourcing networks: challenges and opportunities
(2015)

The mobile crowdsourcing network (MCN) is a promising network architecture that applies the principles of crowdsourcing to perform tasks with human involvement and powerful mobile devices. However, it also raises some critical security and privacy issues that impede the application of MCNs. In this article, in order to better understand these critical security and privacy challenges, we first propose a general architecture for a mobile crowdsourcing network comprising both crowdsourcing sensing and crowdsourcing computing. After that, we set forth several critical security and privacy challenges that essentially capture the characteristics of MCNs. We also formulate some research problems leading to possible research directions. We expect this work will bring more attention to further investigation on security and privacy solutions for mobile crowdsourcing networks.
DAC-MACS: Effective Data Access Control for Multiauthority Cloud Storage Systems
(2012)

Data access control is an effective way to ensure data security in the cloud. However, due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Existing access control schemes are no longer applicable to cloud storage systems, because they either produce multiple encrypted copies of the same data or require a fully trusted cloud server. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising technique for access control of encrypted data. However, due to the inefficiency of decryption and revocation, existing CP-ABE schemes cannot be directly applied to construct a data access control scheme for multiauthority cloud storage systems, where users may hold attributes from multiple authorities. In this paper, we propose data access control for multiauthority cloud storage (DAC-MACS), an effective and secure data access control scheme with efficient decryption and revocation. Specifically, we construct a new multiauthority CP-ABE scheme with efficient decryption, and also design an efficient attribute revocation method that can achieve both forward security and backward security. We further propose an extensive data access control scheme (EDAC-MACS), which is secure under weaker security assumptions.